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Masco

Designs and manufactures branded home products

Data Engineer

Data EngineeringUpdated on Nov 4
Full-timeIndiaNo salary listedIn-person

About the Job

Requirements

  • A Bachelor's or Master's degree in Computer Science, Engineering, Data Analytics, or a related field, or equivalent professional experience.
  • Substantial hands-on experience designing and building data engineering solutions on a major cloud platform, with emphasis on the Azure ecosystem.
  • Experience delivering production pipelines and supporting them in an operational setting.
  • Advanced hands-on expertise with Databricks and the Azure data stack, including Data Factory, Data Lake, Synapse, and Analysis Services.
  • Strong working knowledge of medallion architecture (bronze, silver, and gold) for structuring Lakehouse solutions.
  • Familiarity with Microsoft Fabric and its role alongside Databricks in a modern enterprise data platform.
  • Advanced SQL and T-SQL skills, including queries, stored procedures, functions, indexes, partitions, DDL, DML, and strong ETL/ELT design and build experience.
  • Strong proficiency in Python or Scala for data engineering.
  • Experience with RESTful API development for data acquisition and ingestion from retailer and third-party sources.
  • Working knowledge of distributed processing with Apache Spark and Lakehouse principles, including ACID storage, schema enforcement, and versioning.
  • Solid understanding of CI/CD, DevOps, and automation for data workflows.
  • Strong understanding of Kimball dimensional modeling, including snapshot and transactional fact tables and conforming dimensions.
  • Working knowledge of data constructs including keys, constraints, data types, normalization, referential integrity, and data quality analysis.
  • Understanding of data governance, security, and compliance as they apply to data engineering.
  • Ability to work independently with minimal oversight on complex, hands-on data work.
  • Ability to explore and understand new and modern data tools relevant to enterprise POS and related engineering work.
  • Ability to explain technical details clearly to engineering and analytics peers.

Responsibilities

  • Design, build, and maintain automated ingestion pipelines and multi-source integration for retailer, HQ, and BU data on the Databricks and Azure data stack.
  • Develop RESTful APIs and API-based integrations for retailer and third-party data acquisition.
  • Deliver critical-path pipelines and validation for priority data feeds.
  • Integrate orchestration tools and cloud or hybrid storage systems to enable end-to-end data workflows.
  • Contribute to Lakehouse solutions using ACID-compliant storage layers, schema enforcement, and versioning for reliable data management.
  • Run pipeline-level data-quality checks for missing values, outliers, consistency, and cross-source reconciliation.
  • Implement monitoring, validation, and automated notifications within the data lifecycle to protect performance, quality, and availability.
  • Support pipeline monitoring, incident response, and continuous improvement in partnership with the Data Engineering Leader.
  • Maintain change-control and testing processes for pipeline and data-model modifications.
  • Build the ingestion side of the attribution crosswalk and master data foundations against the Enterprise Data Architect's documented specification.
  • Support maintenance of hierarchy mappings and attribute relationships through pipeline work.
  • Surface data-quality issues, mapping exceptions, and structural gaps to the Data Engineering Leader and Enterprise Data Architect.
  • Implement CI/CD pipelines for data engineering workflows and automate testing, deployment, and monitoring of data solutions.
  • Develop, maintain, and secure tables, relationships, metrics, and calculations for analytical models supporting reporting and advanced analytics.
  • Contribute research and recommendations on data management products, services, and standards.
  • Document ingestion patterns, pipelines, validation rules, and metric definitions as part of the definition of done.
  • Contribute pipeline lineage and technical metadata to the enterprise catalog.
  • Follow documentation standards established by the Data Engineering Leader and Enterprise Data Architect.
  • Collaborate with the Data Engineering Leader, Enterprise Data Architect, and BI Developers on shared pipelines, models, and metric definitions.

Desired Qualifications

  • Experience in retail, consumer goods, or manufacturing analytics environments where POS, sell-in, inventory, and third-party retail data are core.
  • Experience with Databricks Unity Catalog, data lineage, and observability tools.
  • Familiarity with machine learning and artificial intelligence integration, including MLflow and Azure ML, and DataOps/MLOps practices.
  • Experience with Microsoft Tabular models and the DAX language.
  • Experience with at least one data analysis tool such as Power BI or Tableau.

About the Company

Masco

Designs and manufactures branded home products

Masco Corporation designs, manufactures, and distributes branded home-improvement and building products around the world. Its product lines include Behr paint, Delta and Hansgrohe faucets and fixtures, and HotSpring spas, among others. These products are produced and brought to market through Masco’s multiple brands across plumbing and decorative architectural segments, enabling consumers to update living spaces with paints, faucets, hardware, and spa systems. Masco differentiates itself with a broad, well-known brand portfolio and global reach, combining design, manufacturing, and distribution under one umbrella to serve homes, environments, and communities. The company aims to improve living possibilities for customers while supporting environmental sustainability and community growth, and to generate returns for shareholders as it expands housing accessibility and community initiatives.

Company size
5,001-10,000
Company stage
IPO
Headquarters
Livonia, MI
Founded
1929

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